Merge pull request #86 from WenjieDu/dev

Make each model a single package

pypots/__init__.py

@@ -24,13 +24,19 @@
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 __version__ = "0.0.11"
 
+from pypots import classification
+from pypots import clustering
+from pypots import data
+from pypots import forecasting
+from pypots import imputation
+from pypots import utils
 
 __all__ = [
- "data",
- "imputation",
  "classification",
  "clustering",
+ "data",
  "forecasting",
+ "imputation",
  "utils",
  "__version__",
 ]

pypots/classification/brits/__init__.py

@@ -0,0 +1,12 @@
+"""
+
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from pypots.classification.brits.model import BRITS
+
+__all__ = [
+ "BRITS",
+]

pypots/classification/brits/dataset.py

@@ -0,0 +1,48 @@
+"""
+Dataset class for model BRITS.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from typing import Union
+
+from pypots.imputation.brits.dataset import (
+ DatasetForBRITS as DatasetForBRITS_Imputation,
+)
+
+
+class DatasetForBRITS(DatasetForBRITS_Imputation):
+ """Dataset class for BRITS.
+
+ Parameters
+ ----------
+ data : dict or str,
+ The dataset for model input, should be a dictionary including keys as 'X' and 'y',
+ or a path string locating a data file.
+ If it is a dict, X should be array-like of shape [n_samples, sequence length (time steps), n_features],
+ which is time-series data for input, can contain missing values, and y should be array-like of shape
+ [n_samples], which is classification labels of X.
+ If it is a path string, the path should point to a data file, e.g. a h5 file, which contains
+ key-value pairs like a dict, and it has to include keys as 'X' and 'y'.
+
+ return_labels : bool, default = True,
+ Whether to return labels in function __getitem__() if they exist in the given data. If `True`, for example,
+ during training of classification models, the Dataset class will return labels in __getitem__() for model input.
+ Otherwise, labels won't be included in the data returned by __getitem__(). This parameter exists because we
+ need the defined Dataset class for all training/validating/testing stages. For those big datasets stored in h5
+ files, they already have both X and y saved. But we don't read labels from the file for validating and testing
+ with function _fetch_data_from_file(), which works for all three stages. Therefore, we need this parameter for
+ distinction.
+
+ file_type : str, default = "h5py"
+ The type of the given file if train_set and val_set are path strings.
+ """
+
+ def __init__(
+ self,
+ data: Union[dict, str],
+ return_labels: bool = True,
+ file_type: str = "h5py",
+ ):
+ super().__init__(data, return_labels, file_type)

pypots/classification/brits.py → pypots/classification/brits/model.py

@@ -13,8 +13,11 @@
 from torch.utils.data import DataLoader
 
 from pypots.classification.base import BaseNNClassifier
-from pypots.data import DatasetForBRITS
-from pypots.imputation.brits import RITS as imputation_RITS, _BRITS as imputation_BRITS
+from pypots.classification.brits.dataset import DatasetForBRITS
+from pypots.imputation.brits.model import (
+ RITS as imputation_RITS,
+ _BRITS as imputation_BRITS,
+)
 
 
 class RITS(imputation_RITS):

pypots/classification/grud/__init__.py

@@ -0,0 +1,12 @@
+"""
+
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from pypots.classification.grud.model import GRUD
+
+__all__ = [
+ "GRUD",
+]

pypots/classification/grud.py → pypots/classification/grud/model.py

@@ -15,8 +15,8 @@
 from torch.utils.data import DataLoader
 
 from pypots.classification.base import BaseNNClassifier
-from pypots.data.dataset_for_grud import DatasetForGRUD
-from pypots.imputation.brits import TemporalDecay
+from pypots.classification.grud.dataset import DatasetForGRUD
+from pypots.imputation.brits.module import TemporalDecay
 
 
 class _GRUD(nn.Module):

pypots/classification/raindrop/__init__.py

@@ -0,0 +1,12 @@
+"""
+
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from pypots.classification.raindrop.model import Raindrop
+
+__all__ = [
+ "Raindrop",
+]

pypots/classification/raindrop/dataset.py

@@ -0,0 +1,47 @@
+"""
+Dataset class for model Raindrop.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+
+from typing import Union
+
+from pypots.classification.grud.dataset import DatasetForGRUD
+
+
+class DatasetForRaindrop(DatasetForGRUD):
+ """Dataset class for model GRU-D.
+
+ Parameters
+ ----------
+ data : dict or str,
+ The dataset for model input, should be a dictionary including keys as 'X' and 'y',
+ or a path string locating a data file.
+ If it is a dict, X should be array-like of shape [n_samples, sequence length (time steps), n_features],
+ which is time-series data for input, can contain missing values, and y should be array-like of shape
+ [n_samples], which is classification labels of X.
+ If it is a path string, the path should point to a data file, e.g. a h5 file, which contains
+ key-value pairs like a dict, and it has to include keys as 'X' and 'y'.
+
+ return_labels : bool, default = True,
+ Whether to return labels in function __getitem__() if they exist in the given data. If `True`, for example,
+ during training of classification models, the Dataset class will return labels in __getitem__() for model input.
+ Otherwise, labels won't be included in the data returned by __getitem__(). This parameter exists because we
+ need the defined Dataset class for all training/validating/testing stages. For those big datasets stored in h5
+ files, they already have both X and y saved. But we don't read labels from the file for validating and testing
+ with function _fetch_data_from_file(), which works for all three stages. Therefore, we need this parameter for
+ distinction.
+
+ file_type : str, default = "h5py"
+ The type of the given file if train_set and val_set are path strings.
+ """
+
+ def __init__(
+ self,
+ data: Union[dict, str],
+ return_labels: bool = True,
+ file_type: str = "h5py",
+ ):
+ super().__init__(data, return_labels, file_type)